Risk rating systems typically use a scorecard, which incorporates several characteristics of the credit and borrower and converts those into a numerical rating of potential future payment volatility.
Many banks and credit unions use the traditional 5 C’s of credit to develop a scorecard:
- Capacity. Capacity assesses the borrower’s ability to repay the loan by comparing expected income to the recurring debt for which the borrower will be responsible. Credit analysis often uses debt service coverage and debt-to-income ratios to evaluate capacity.
- Collateral. Collateral is often evaluated using loan-to-value and committed loan-to-value ratios. It should provide assurance that if the borrower defaults, the lender will have the opportunity to recoup potential losses.
- Conditions. Assessing the conditions of the credit traditionally involves evaluating the terms of the loan (principal balance, payment terms, interest rate) to ensure the financial institution is getting paid for the risk it is assuming with the credit.
- Character. Traditionally, character refers to the borrower’s track record of repaying debt, so credit reports and FICO scores are common factors incorporated.
- Capital. Evaluating capital during credit analysis typically includes considering the down payment or any other amount the borrower has put toward the investment that requires the loan.
One key element of a scorecard is that it IS a scorecard. It is a form of measurement (in this case, gradations of risk). To achieve a final score, one or more models may (or may not) be employed, depending on the complexity of the portfolio, to arrive at results for the various components of measurement. However, the scorecard itself is not a model. Therefore, an effectively designed scorecard is not necessarily subject to the rigors of model validation.
Updating the traditional 5 C’s of credit
Borrower behavior and the lending environment have changed dramatically in recent years, especially since COVID. As a result, Newberry recommends providing additional focus on two other areas during credit rating:
Concentrations. Concentrated risks in a business – whether related to customers, supply chains, or other factors – resulted in increased credit risk during the pandemic when those risks affected businesses’ ability to generate cash.
Cash. More borrowers have walked away from underwater assets in recent years. Look beyond down payments and consider the borrower’s ability to repay from a cash flow perspective. “What kind of debt load do they carry? Do they have enough current assets and cash to cover their current liabilities?” said Newberry. “How has the pandemic impacted income or cash flows? Is the borrower in an industry that is more at risk due to expected lags in economic recovery time?”
Creating an objective and subjective risk rating system
Regardless of whether it uses the traditional C’s or a revised list, an institution should incorporate three types of analysis into its loan grading:
Objective credit risk rating analysis assesses financial health and involves:
- Getting financial statements on an annual basis
- Conducting financial ratio analysis
- Comparing the borrower to an industry benchmark or standard industry data.
Objective components for grading credit promote consistency in ratings across the lending staff. Quantitative inputs can provide objectivity. Global cash flow, global debt service coverage, global debt to equity, financial statement strength and loan to value, and collateral value for the loan are among ratios commonly analyzed. Future performance after the credit is approved is important to evaluate, too, so Kirby advised including those projections the rating framework as well. For example, a company may be a 4 rated credit prior to acquiring another company, but after taking on the debt to affect the transaction, the rating will decline to a 6. The correct rating at the time of underwriting (before funds are disbursed) is 6, not 4. Don’t wait until the transaction transpires; recognize the risk that the bank is taking.
Industry comparisons can reveal strengths and weaknesses of the specific credit. For example, a dental practice could buck negative trends in the industry because it is the best-run practice in the market. Industry data can also uncover differences between industries to help evaluate a borrower in an industry not already served by the institution. Logging and dairy farming, for example, require a lot of cash and investment upfront, while other types of businesses may not.
Subjective risk-evaluation components
Some aspects of evaluating creditworthiness (such as management strength and strength of guarantors) are more subjective or qualitative. Having significant qualitative risk weightings included in a risk grade adds complexity.
“One issue is that you lose consistency across your lending team,” Newberry said. “Two people should have the exact same number coming off the scorecard.” Subjective components increase the chances that one lender presenting a borrower to the loan committee could get the loan approved but another lender might not. “That’s where examiners have issues,” he added.
Subjective factors also complicate how to change the grade following changes in debt service coverage ratio or net income. “That impacts downstream capabilities, such as loan migration and stress testing,” Newberry said. “And that issue moves down the line into total losses, impact on net income, and impact on capital.”
He said examiners had emphasized being able to stress test debt service and changes in collateral valuations, so objective analysis is critical.
Subjective credit risk rating systems aren’t an early warning system,” Kirby said. “They’re just not. You have to have an objective base framework in order to have that early warning system.”
And regardless of the regulatory environment, “having an early warning system is important from a good management standpoint,” he said.
This post is an update to an article published in 2022.